Consumer & Retail

Company Profile

  • €300m annual revenue
  • Home accessories and furniture retail chain operating in several countries across Europe
  • 5,000 SKUs

Situation and Objectives

  • Develop concept with an analytical algorithm to identify optimal mark-down price
  • Reduce total cost of ownership by minimizing mark-downs and other follow-up cost


  • Build up a database covering all marked-down items across all categories for the last four years
  • Define mark-down guidelines based on the company strategy as side conditions for an optimal mark-down algorithm
  • Calculate mark-down elasticities based on time-series regressions accounting for internal (e.g. store size, price level) and external factors (e.g. weather, season, holiday, payday)
  • Code algorithm that recommends category manager optimal mark-down price for items with a sales ratio below target
  • Test concept to fine-tune mark-down algorithm

System Based Mark-Down Pricing to End-Up with Profit-Optimal Closing Stocks


  • Apply pricing grid with psychological optimal price points
  • Stop "scattergun approach" to manage mark-downs (i.e. item identification and mark-down level)
  • Set mark-down price based on forecasted sales volume and related total cost of ownership
  • Use expert judgment in a structured way if data is not sufficient to mathematically forecast sales volume


  • Reduced discount level for mark-downs by 6%
  • Reduced costs for eliminating remaining stock by 13%
  • Reduced total cost of ownership by 8%
  • Improved revenue by 3%